基于鲸鱼算法的旱地春小麦农田土壤N2O排放模型参数优化

doi:10.3864/j.issn.0578-1752.2025.03.010

摘要/Abstract

摘要：

【目的】采用鲸鱼优化算法（Whale Optimization Algorithm，WOA）对APSIM模型中与土壤N₂O排放相关的默认参数进行优化，以提高模型在模拟我国西北半干旱农业区土壤N₂O排放的准确性和适用性，为精确评估和管理农业活动中的温室气体排放提供支持。【方法】采用2020—2021年甘肃省定西市安定区安家坡旱农综合长期定位试验站测定的田间试验数据，结合气象局提供的1970—2021年气象数据，对APSIM模型中N₂O形成阶段的4个关键参数（土壤硝化潜力nitrification_pot、铵态氮在半最大利用效率时的浓度nh4_at_half_pot、反硝化系数dnit_rate_coeff、反硝化水系数计算的幂项dnit_wf_power），采用鲸鱼优化算法（WOA）进行单目标多参数优化。通过比较APSIM模型默认参数模拟值、优化参数模拟值和实测值的误差，评估优化后的APSIM土壤N₂O排放模型的准确性。【结果】通过多次执行优化程序，最终获得了4个参数的最优组合。其中，土壤硝化潜力为7.62 mg·kg^-1·d^-1、铵态氮在半最大利用效率时的浓度为49.3 mg·kg^-1、反硝化系数为0.00063、反硝化水系数计算的幂项为0.64。与APSIM模型的默认参数相比，决定系数R²从0.432提升至0.719，均方根误差RMSE从39.42 μg·m^-2·h^-1减少至25.37 μg·m^-2·h^-1，归一化均方根误差NRMSE从18.51%下降至11.92%。鲸鱼算法在优化过程中展现出显著的全局搜索能力和快速收敛性。优化后的APSIM模型在模拟土壤N₂O排放方面的精度显著提高，表明该方法可以实现对模型参数的快速准确率定。【结论】通过应用鲸鱼算法，4个关键参数得到了精确调整，使得模型的预测误差显著减小，明显地改善了APSIM模型在模拟土壤N₂O排放的性能。优化后的模型在我国西北半干旱农业区表现出更高的准确性和适用性，同时也证明了该优化策略的有效性。

关键词: N₂O排放, APSIM模型, 参数优化, 鲸鱼优化算法, 春小麦, 旱地土壤

Abstract:

【Objective】In order to improve the simulation accuracy of N₂O emissions by using APSIM model, this study used Whale Optimization Algorithm (WOA) to optimize the default parameters related to soil N₂O emissions in the APSIM model to improve the accuracy and applicability of the model in simulating soil N₂O emissions in the semi-arid agricultural region of northwest China, for providing support for precise assessment and management of greenhouse gas emissions in agricultural activities. 【Method】This study used field experimental data measured by the Anjiapo integrated long-term positioning test station in Anding District, Dingxi City, Gansu Province from 2020 to 2021, combined with meteorological data provided by the Meteorological Bureau from 1970 to 2021, to optimize the four key parameters of N₂O formation stage in the APSIM model (soil nitrification potential (nitration_pot), concentration of ammonium nitrogen at semi maximum utilization efficiency (nh4_at-half_pot), denitrification coefficient (dnit_rate-coeff), and power term of denitrification water coefficient (dnit_wf_power) using the WOA for single objective and multi parameter optimization. The accuracy of the optimized APSIM soil N₂O emission model was evaluated by comparing the errors between the default parameter simulation values, optimized parameter simulation values, and measured values of the APSIM model. 【Result】Through multiple executions of the optimization program, the optimal combination of four parameters was ultimately determined. Among them, the soil nitrification potential was 7.62 mg·kg^-1·d^-1, the concentration of ammonia nitrogen at semi maximum utilization efficiency was 49.3 mg·kg^-1, the denitrification coefficient was 0.00063, and the power term of the denitrification water coefficient calculation was 0.64. Compared with the default parameters of the APSIM model, the coefficient of determination R² increased from 0.432 to 0.719, the root mean square error (RMSE) decreased from 39.42 to 25.37 μg·m^-2·h^-1, and the normalized root mean square error (NRMSE) decreased from 18.51% to 11.92%. The whale algorithm exhibited significant global search capability and fast convergence during the optimization process. The optimized APSIM model significantly improved the accuracy of simulating soil N₂O emissions, indicating that this method could achieve rapid and accurate calibration of model parameters. 【Conclusion】By applying WOA, four key parameters were precisely adjusted, which significantly reduced the prediction error of the model and significantly improving the performance of the APSIM soil N₂O emission model. The optimized model has shown higher accuracy and applicability in the semi-arid agricultural region of northwest China, which also proved the effectiveness of the optimization strategy.

Key words: N₂O emission, APSIM model, parameter optimization, Whale Optimization Algorithm (WOA), spring wheat, dryland soil

牟树佳, 董莉霞, 李广, 燕振刚, 逯玉兰. 基于鲸鱼算法的旱地春小麦农田土壤N₂O排放模型参数优化[J]. 中国农业科学, 2025, 58(3): 537-547.

MU ShuJia, DONG LiXia, LI Guang, YAN ZhenGang, LU YuLan. Optimization of N₂O Emission Parameters in Dryland Spring Wheat Farmland Soil Based on Whale Optimization Algorithm[J]. Scientia Agricultura Sinica, 2025, 58(3): 537-547.

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参数 Parameter	土壤深度 Soil depth (cm)
参数 Parameter	0-5	5-10	10-30	30-50	50-80	80-110	110-140	140-170	170-200
容重Bulk density (g·cm^-3)	1.29	1.23	1.33	1.20	1.14	1.14	1.25	1.12	1.11
风干含水量 Air-dried moisture (mm·mm^-1)	0.01	0.01	0.05	0.07	0.09	0.10	0.11	0.12	0.13
萎蔫系数 Wilting coefficient (mm·mm^-1)	0.09	0.09	0.09	0.09	0.09	0.11	0.11	0.12	0.13
田间持水量 Field capacity (mm·mm^-1)	0.27	0.27	0.27	0.27	0.26	0.27	0.26	0.26	0.26
饱和含水量 Saturated moisture (mm·mm^-1)	0.46	0.49	0.45	0.50	0.52	0.52	0.48	0.53	0.53
有效水分下限 Lower available moisture (mm·mm^-1)	0.09	0.09	0.09	0.09	0.10	0.12	0.13	0.18	0.2

参数Parameter	默认值Default	范围Range
土壤硝化潜力Soil nitrification potential (mg·kg^-1·d^-1)	40	6—60
铵态氮在半最大利用效率时的浓度 The concentration of ammonia nitrogen at semi maximum utilization efficiency (mg·kg^-1)	90	6—186
反硝化系数Denitrification coefficient	0.0006	0.0005—0.0018
反硝化水系数计算的幂项P Power term P for calculating denitrification water coefficient	1	0.5—5

模型参数 Model parameter	决定系数 R²	均方根误差 RMSE (μg·m^-2·h^-1)	归一化均方根误差 NRMSE (%)
默认值Default	0.423	39.42	18.51
优化后After optimization	0.719	25.37	11.92

基于鲸鱼算法的旱地春小麦农田土壤N₂O排放模型参数优化

Optimization of N₂O Emission Parameters in Dryland Spring Wheat Farmland Soil Based on Whale Optimization Algorithm

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参数Parameter	默认值Default	优化值Optimization
土壤硝化潜力Soil nitrification potential (mg·kg^-1·d^-1)	40	7.62
铵态氮在半最大利用效率时的浓度 The concentration of ammonia nitrogen at semi maximum utilization efficiency (mg·kg^-1)	90	49.3
反硝化系数Denitrification coefficient	0.0006	0.00063
反硝化水系数计算的幂项P Power term P for calculating denitrification water coefficient	1	0.64

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